Juan Li
Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 35-48, 2019, DOI:10.31209/2018.100000003
Abstract K-Nearest Neighbor algorithm (KNN) is a simple and mature classification
method. However there are susceptible factors influencing the classification
performance, such as k value determination, the overlarge search space,
unbalanced and multi-class patterns, etc. To deal with the above problems, a
new classification algorithm that absorbs tree structure, tree pruning and
adaptive k value method was proposed. The proposed algorithm can
overcome the shortcoming of KNN, improve the performance of multi-class
and unbalanced classification, reduce the scale of dataset maintaining the
comparable classification accuracy. The simulations are conducted and the
proposed algorithm is compared with several existing More >
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